Global Patent Index - EP 4064186 A1

EP 4064186 A1 20220928 - METHODS AND SYSTEMS FOR GENERATING SYNTHETIC MICROSTRUCTURE IMAGES OF MATERIAL WITH DESIRED FEATURES

Title (en)

METHODS AND SYSTEMS FOR GENERATING SYNTHETIC MICROSTRUCTURE IMAGES OF MATERIAL WITH DESIRED FEATURES

Title (de)

VERFAHREN UND SYSTEME ZUR ERZEUGUNG SYNTHETISCHER MIKROSTRUKTURBILDER VON MATERIAL MIT GEWÜNSCHTEN EIGENSCHAFTEN

Title (fr)

PROCÉDÉS ET SYSTÈMES PERMETTANT DE GÉNÉRER DES IMAGES DE MICROSTRUCTURE SYNTHÉTIQUE DE MATÉRIAU PRÉSENTANT DES CARACTÉRISTIQUES SOUHAITÉES

Publication

EP 4064186 A1 20220928 (EN)

Application

EP 21187439 A 20210723

Priority

IN 202121012275 A 20210322

Abstract (en)

The disclosure generally relates to methods and systems for generating synthetic microstructure images of a material with desired features. Conventional techniques that make use of unsupervised deep generative models has no control on the generated microstructure images with specific, desired set of features. The present disclosure generates the synthetic microstructure images of the material with desired feature, by using a variational autoencoder defined with a style loss function. In the first step, the variational autoencoder is trained to learn latent representation of microstructure image of the material. In the second step, some of the dimensions of learned latent representation is interpreted as physically significant features. In the third and last step, the latent representation required for getting the desired features is appropriately sampled based on the interpretation to generate the synthetic microstructure images of the material with desired features.

IPC 8 full level

G06T 7/00 (2017.01)

CPC (source: EP US)

G06F 18/214 (2023.01 - US); G06F 18/217 (2023.01 - US); G06F 18/22 (2023.01 - US); G06T 7/0004 (2013.01 - EP); G06V 10/74 (2022.01 - US); G06V 10/774 (2022.01 - US); G06V 10/776 (2022.01 - US); G06T 2207/10056 (2013.01 - EP); G06T 2207/20021 (2013.01 - EP); G06T 2207/20081 (2013.01 - EP); G06T 2207/20084 (2013.01 - EP)

Citation (search report)

  • [IY] XIAO LI ET AL: "Latent Space Factorisation and Manipulation via Matrix Subspace Projection", ARXIV.ORG, CORNELL UNIVERSITY LIBRARY, 201 OLIN LIBRARY CORNELL UNIVERSITY ITHACA, NY 14853, 26 July 2019 (2019-07-26), XP081450922
  • [Y] SNELL JAKE ET AL: "Learning to generate images with perceptual similarity metrics", 2017 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP), IEEE, 17 September 2017 (2017-09-17), pages 4277 - 4281, XP033323382, DOI: 10.1109/ICIP.2017.8297089
  • [Y] MINYOUNG KIM ET AL: "Relevance Factor VAE: Learning and Identifying Disentangled Factors", ARXIV.ORG, CORNELL UNIVERSITY LIBRARY, 201 OLIN LIBRARY CORNELL UNIVERSITY ITHACA, NY 14853, 5 February 2019 (2019-02-05), XP081025413
  • [Y] EMILIEN DUPONT: "Learning Disentangled Joint Continuous and Discrete Representations", ARXIV.ORG, CORNELL UNIVERSITY LIBRARY, 201 OLIN LIBRARY CORNELL UNIVERSITY ITHACA, NY 14853, 31 March 2018 (2018-03-31), XP081420996
  • [A] MICHAEL MATHIEU ET AL: "Disentangling factors of variation in deep representations using adversarial training", ARXIV.ORG, CORNELL UNIVERSITY LIBRARY, 201 OLIN LIBRARY CORNELL UNIVERSITY ITHACA, NY 14853, 10 November 2016 (2016-11-10), XP080730837
  • [A] ZHENG ZHILIN ET AL: "Disentangling Latent Space for VAE by Label Relevant/Irrelevant Dimensions", 2019 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR), IEEE, 15 June 2019 (2019-06-15), pages 12184 - 12193, XP033686819, DOI: 10.1109/CVPR.2019.01247

Designated contracting state (EPC)

AL AT BE BG CH CY CZ DE DK EE ES FI FR GB GR HR HU IE IS IT LI LT LU LV MC MK MT NL NO PL PT RO RS SE SI SK SM TR

Designated extension state (EPC)

BA ME

DOCDB simple family (publication)

EP 4064186 A1 20220928; US 11663295 B2 20230530; US 2022343116 A1 20221027

DOCDB simple family (application)

EP 21187439 A 20210723; US 202117387880 A 20210728